Novartis Institutes for BioMedical Research (NIBR)
Connecting the dots in early drug discoveryStephan ReilingSenior Scientist, Novartis Institutes for BioMedical Research
Connecting the dots in early drug discoveryStephan ReilingIn-Silico Lead Discovery GroupNovartis Institutes for BioMedical Research (NIBR) Cambridge
GraphConnect 2016, San Francisco
Novartis Institutes for BioMedical Research (NIBR)
Novartis Institutes for BioMedical Research (NIBR)
Why (might you be interested in this talk)
• The talk shows how a lot of heterogeneous data can be integrated into one big graph– Greater than the sum of its parts
• Text mining and pattern detection can lead to valuable insights– Nobody can read 25 million scientific papers
• Data mining this graph can give novel biological insights– Connecting the dots
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Novartis Institutes for BioMedical Research (NIBR)
Why (did we build the graph)
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Treatment effects in cellular phenotypic assays
Compound treatment
Novartis Institutes for BioMedical Research (NIBR)
• What we have (the dots)– almost 1 Billion data points of
compound activity data on protein targets (~99% of which can be summarized as “not active”)
– More and more results of phenotypic assays
• What we lack (the connections)– A good way to use biological
knowledge or background information to make a connection
– A storage for “biological knowledge” that can be “queried”
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Why
Compound
GeneDisease(Phenotype)
Novartis Institutes for BioMedical Research (NIBR)
How (did we build the graph)
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Text mining for chemicals, diseases, proteins
In continuation of our investigation on novel stearoyl-CoA desaturase (SCD) 1 inhibitors, we have already reported on the structural modification of the benzoylpiperidines that led to a series of novel and highly potent spiropiperidine-based SCD1 inhibitors. In this report, we would like to extend the scope of our previous investigation and disclose details of the synthesis, SAR, ADME, PK, and pharmacological evaluation of the spiropiperidines with high potency for SCD1 inhibition. Our current efforts have culminated in the identification of 5-fluoro-1'-{6-[5-(pyridin-3-ylmethyl)-1,3,4-oxadiazol-2-yl]pyridazin-3-yl}-3,4-dihydrospiro[chromene-2,4'-piperidine] (10e), which demonstrated a very strong potency for liver SCD1inhibition (ID(50)=0.6 mg/kg). This highly efficacious inhibition is presumed to be the result of a combination of strong enzymatic inhibitory activity (IC(50) (mouse)=2 nM) and good oral bioavailability (F >95%). Pharmacological evaluation of 10e has demonstrated potent, dose-dependent reduction of the plasma desaturation index in C57BL/6J mice on a high carbohydrate diet after a 7-day oral administration (q.d.). In addition, it did not cause any noticeable skin abnormalities up to the highest dose (10 mg/kg).
Novartis Institutes for BioMedical Research (NIBR)
How (did we build the graph)
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Text mining for chemicals, diseases, proteins
In continuation of our investigation on novel stearoyl-CoA desaturase (SCD) 1 inhibitors, we have already reported on the structural modification of the benzoylpiperidines that led to a series of novel and highly potent spiropiperidine-based SCD1 inhibitors. In this report, we would like to extend the scope of our previous investigation and disclose details of the synthesis, SAR, ADME, PK, and pharmacological evaluation of the spiropiperidines with high potency for SCD1 inhibition. Our current efforts have culminated in the identification of 5-fluoro-1'-{6-[5-(pyridin-3-ylmethyl)-1,3,4-oxadiazol-2-yl]pyridazin-3-yl}-3,4-dihydrospiro[chromene-2,4'-piperidine] (10e), which demonstrated a very strong potency for liver SCD1inhibition (ID(50)=0.6 mg/kg). This highly efficacious inhibition is presumed to be the result of a combination of strong enzymatic inhibitory activity (IC(50) (mouse)=2 nM) and good oral bioavailability (F >95%). Pharmacological evaluation of 10e has demonstrated potent, dose-dependent reduction of the plasma desaturation index in C57BL/6J mice on a high carbohydrate diet after a 7-day oral administration (q.d.). In addition, it did not cause any noticeable skin abnormalities up to the highest dose (10 mg/kg).
Hit Type Recognized text SmilesT1 GeneOrProtein stearoyl-CoA desaturaseT2 Mechanism inhibitorsT3 G benzoylpiperidines
T4 D spiropiperidine O=C(NC(Cc1c[nH]c2ccccc12)C(=O)N3CCC4(CC3)CCc5ccccc45)NC6CN7CCC6CC7
T5 GeneOrProtein SCD1T6 Mechanism inhibitorsT7 GeneOrProtein SCD1
T8 M 5-fluoro-1'-{6-[5-(pyridin-3-ylmethyl)-1,3,4-oxadiazol-2-
yl]pyridazin-3-yl}-3,4-dihydrospiro[chromene-2,4'-piperidine]
FC1=C2CCC3(OC2=CC=C1)CCN(CC3)C=3N=NC(=CC3)C=3OC(=NN3)CC=3C=NC=CC3
T9 GeneOrProtein SCD1T10 G carbohydrateT11 Disease skin abnormalities
Novartis Institutes for BioMedical Research (NIBR)
How (did we build the graph)
• ~25,000,000 article abstracts
• 5,600 journals
• 1946 – current
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National Institutes of Health (NIH) PubMed http://www.ncbi.nlm.nih.gov/pubmed
http://www.ncbi.nlm.nih.gov/pubmed/?term=20801551
• Tagged with “MeSH terms”(MeSH: Medical Subject Heading)
Novartis Institutes for BioMedical Research (NIBR)
How
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Structure of the MeSH term hierarchy (partial)
Yellow: DiseasesBlue: Processes and MechanismsGreen: AnatomyRed: Chemicals and DrugsGrey: Organisms
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Novartis Institutes for BioMedical Research (NIBR)Public11
Novartis Institutes for BioMedical Research (NIBR)
How
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Association rule mining of co-occurrences
Article 1• Compound A• Gene 1• Gene 2
Article 2• Compound A• Compound B• Gene 1
Article 3• Compound A• Mesh term X• Gene 1
Article 4• Compound C• Gene 1
• Identification of entities (compounds, mesh terms, genes, diseases,…) from pubmed annotations or textmining
• The a-priori algorithm from association rule mining is used to identify frequently co-mentioned entities (aka market basket analysis)
• Associations above a certain association strength (lift) and number of articles in which they are co-mentioned (support) are stored
• The association strength is scaled to 0-1 and stored as the uncertainty of the association (high lift = low uncertainty)
• Articles are stored as well, including the entities that are mentioned in it
• This only captures the fact that something is frequently co-mentioned with something else, not any causality (similar to correlation)
Novartis Institutes for BioMedical Research (NIBR)
What (can you do with this)
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Example: disease – compound – target from text miningEvery relationship in the graph has a property “uncertainty” in the range of 0-1This allows to query for connections with the highest confidence
Tafamidis (INN, or Fx-1006A, trade name Vyndaqel) is a drug for the amelioration of transthyretin-relatedhereditary amyloidosis (also familial amyloid polyneuropathy, or FAP), a rare but deadly neurodegenerative disease.
Canavan disease is caused by a defective ASPA gene which is responsible for the production of the enzyme aspartoacylase. Decreased aspartoacylase activity prevents the normal breakdown of N-acetyl aspartate, wherein the accumulation of N-acetylaspartate, or lack of its further metabolism interferes with growth of the myelin sheath of the nerve fibers of the brain.
From Wikipedia: From Wikipedia:
Color code: Disease, Gene, Compound
MATCH p = (cpd:Compound) -[:is_associated]-> (g:Gene) -[:is_associated]-> (d:Disease) <-[:is_associated]- (cpd)
RETURN p, reduce(u=0.0, r in relationships(p) | u+r.uncertainty) as uncORDER BY unc
Novartis Institutes for BioMedical Research (NIBR)
What (can you do with this)
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So why not just load Wikipedia?
Disease Uncertainty
Canavan Disease 0.1
Pelizaeus-Merzbacher Disease 0.364
Alexander Disease 0.432
Diffuse Axonal Injury 0.432
Brain Diseases, Metabolic 0.451
MATCH p = (cpd:Compound {name: 'N-acetylaspartate'}) -[r:is_associated]-> (m:Disease) RETURN m.name as Disease, r.uncertainty as Uncertainty ORDER BY r.uncertainty LIMIT 5
Novartis Institutes for BioMedical Research (NIBR)
What (can you do with this)
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Now this is getting more interesting (for us)
MATCH p = (cpd:Compound {name: 'N-acetylaspartate'}) -[r:is_associated]-> (m:CellularComponent)
return m.name as CellularComponent, r.uncertainty as Uncertainty ORDER BY r.uncertainty LIMIT 5
CellularComponent Uncertainty
Axons 0.582
Myelin Sheath 0.611
Extracellular Fluid 0.772
MATCH p = (cpd:Compound {name: 'N-acetylaspartate'}) -[r:is_associated]-> (m:BiologicalProcess)
RETURN m.name as BiologicalProcess, r.uncertainty as Uncertainty ORDER BY r.uncertainty LIMIT 5
BiologicalProcess Uncertainty
Energy Metabolism 0.476
Dominance, Cerebral 0.532
Functional Laterality 0.586
Cerebrovascular Circulation 0.653
Lipid Metabolism 0.72
N-acetylaspartate association with cellular components
N-acetylaspartate association with biological processes
Novartis Institutes for BioMedical Research (NIBR)
Data sources:1. MeSH Hierarchy2. Pubmed articles, (pubmed_id, title,
abstract, Lucene full text searches enabled)
3. Pubmed Associations4. Comparative Toxicogenomics Database
(CTD)5. Compound Target Scores*6. Public compound annotations7. Entity relations from sentences8. Protein-protein interactions data set from
CCSB9. MetaCore gene - gene interactions
(binds, activates, regulates expression, …)10. Similarity relations for all the compounds in
the graph*(~2M compounds)
11. Gene ontology12. Protein annotations13. Pathways / gene sets
Objects:• 25,430,635 articles
• 1,951,819 compounds
• 257,000 Mesh and SCR terms
• 59,859 Genes
• 24,769 GO terms
• 10,570 Diseases
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How (did we build the graph)
Relationships:91 different relationships Compound - is_active – Gene
• X – is_associated – X
• Gene – binding – Gene
• Gene – ubiquitinates – Gene
• Compound – affects_ubiquitination – Gene
• Article – mentions – (compound, gene, mesh)
209,031,615 mentions
50,334,440 is_similar
6,951,257 literature_association
762,002 is_active
Other data sources integrated
(*: NIBR internal data)See Acknowledgments / References slide
30 Million nodes 480 Million relationships
Novartis Institutes for BioMedical Research (NIBR)
How
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The different relationships and nodes in the graph
15 NodesArticle
BiologicalProcessCellType
CellularComponentCompound
DiseaseGene
GeneSetGo
MeshPathway
PfamPhenotypeSimilar2D
Tissue
91 Relationshipsacetylation affects_geranoylation affects_stability is_active
adp_ribosylation affects_glucuronidation affects_sulfation is_associatedaffects_ADP_ribosylation affects_glutathionylation affects_sumoylation is_child_of
affects_N_linked_glycosylation affects_glycation affects_transport is_part_ofaffects_O_linked_glycosylation affects_glycosylation affects_ubiquitination is_query
affects_abundance affects_hydrolysis affects_uptake is_similaraffects_acetylation affects_hydroxylation binding member_of
affects_activity affects_import cleavage mentionsaffects_acylation affects_lipidation co_regulation_of_transcription methylationaffects_alkylation affects_localization complex_formation mirna_bindingaffects_amination affects_metabolic_processing covalent_modification neddylationaffects_binding affects_methylation deacetylation oxidation
affects_carbamoylation affects_mutagenesis demethylation phosphorylationaffects_carboxylation affects_nitrosation deneddylation ppi
affects_chemical_synthesis affects_oxidation dephosphorylation receptor_bindingaffects_cleavage affects_phosphorylation desumoylation s_nitrosylation
affects_cotreatment affects_prenylation deubiquitination sulfationaffects_degradation affects_reaction glycosylation sumoylationaffects_ethylation affects_reduction go_component transcription_regulation
affects_export affects_response_to_substance go_function transformationaffects_expression affects_ribosylation go_process transport
affects_farnesylation affects_secretion gpi_anchor ubiquitinationaffects_folding affects_splicing hydroxylation
Novartis Institutes for BioMedical Research (NIBR)
How (did we build the graph)
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Overall build process
MongoDB PostgreSQL
Pubmedxml files
Internal data sourcesMeSH hierarchies ctdbase PubchemChEMBL ChEBICCSB MetaStore
Information extraction
Compound similaritiesGene sets
Protein annotationsGene ontologies
CSV file staging
TitlesAbstracts
• Information extraction (entity recognition, relationship detection, association rule mining is done on linux cluster)
• Neo4J “endpoint” focused on graph mining
• MongoDB and PostgreSQL are also used for datamining purposes
Neo4J
Novartis Institutes for BioMedical Research (NIBR)
What (can you do with this)
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Example: Analysis of compound activities
A
B
C
D
E
F
G
H
Active compounds Inactive compounds
Novartis Institutes for BioMedical Research (NIBR)
What
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Example: Analysis of compound activities
A
B
C
D
E
F
G
H
2
5
1
43
6
Active compounds Inactive compounds
1. Find genes directly affected by the compounds
Novartis Institutes for BioMedical Research (NIBR)
What
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Example: Analysis of compound activities
A
B
C
D
E
F
G
H
2
8
5
1
4
9
3
6
7
10
Active compounds Inactive compounds
1. Find genes directly affected by the compounds
2. Find all genes that are indirectly affected with some confidence (below a given uncertainyt)
Novartis Institutes for BioMedical Research (NIBR)
What
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Example: Analysis of compound activities
A
B
C
D
E
F
G
H
2
8
5
1
4
9
3
6
7
10
Active compounds Inactive compounds
1. Find genes directly affected by the compounds
2. Find all genes that are indirectly affected with some confidence (below a given uncertainty)
3. Assign nodes that can not be reached a large distance
4. Identify nodes that • can not be reached by
most of the inactive compound
• or are “closer” to the actives than the inactives
Novartis Institutes for BioMedical Research (NIBR)
What
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Example: Analysis of compound activities
MATCH (cpd:Compound)where any( nvs in cpd.cpd_id
where nvs in [‘cpd1’,’cpd2’,…])WITH cpdMATCH p = (cpd) -[r*1..2]-> (m)WITH cpd, p, m, reduce(u=0.0,
r in relationships(p) | u+r.uncertainty) as uncertainty
WHERE uncertainty < 0.9RETURN
cpd.cpd_id as Compound_ID,m.id as ID,uncertainty as Distance
ORDER BY uncertainty
Query reachable nodesCompound_ID Active C582554 C495901 C495900
1 0 1.00 1.00 1.002 1 0.78 0.89 0.883 1 1.00 1.00 1.004 0 1.00 1.00 1.005 0 1.00 0.78 0.676 0 1.00 1.00 1.007 0 1.00 1.00 1.008 0 0.88 0.88 0.909 0 1.00 0.88 0.8210 1 1.00 1.00 1.0011 0 1.00 1.00 1.0012 0 1.00 0.80 0.8313 0 1.00 1.00 1.0014 1 1.00 1.00 1.0015 1 0.82 1.00 1.0016 1 0.78 0.89 0.8817 1 0.80 1.00 1.0018 1 0.80 1.00 1.0019 1 0.78 0.89 0.8820 1 0.80 1.00 1.00
Matrix of compound – node “distances” Result of recursive partitioning(decision tree)
Sum of relationship uncertainty is used as distance from compound to nodeDistance to unreachable node is set to 1.0
( and one surrogate split with equivalent performance: 2 nodes of interest )
Novartis Institutes for BioMedical Research (NIBR)
What
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Example: Analysis of compound activities
Green: relationships derived from in-house data
Grey: relationships found from textmining
Compound1
Compound2
Compound3
Compound4
Compound5
Compound6
Compound7
Compound8
Compound9
Compound10
Compound11
Compound12
Compound13
Only showing the active compounds and their connections to the identified nodes.
Novartis Institutes for BioMedical Research (NIBR)Public25
Compound1
Compound2
Compound3
Compound4
Compound5
Compound6
Compound7
Compound8
Compound9
Compound10
Compound11
Compound12
Compound13
MATCH p = (g1:Gene) -[r*1..2 {datasource: 'metacore'}]-> (g2:Gene)WHERE g2.gene_symbol in ['FOXO','MTOR']
and g1.gene_symbol in ['PRKAB1', 'PRKAA1','PRKAA2']RETURN p, reduce(u=0.0, r in relationships(p) | u+r.uncertainty) as uncORDER BY unc LIMIT 20
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MATCH p = (g1:Gene) <-[:mentions]- (a:Article) -[:mentions]-> (g2:Gene)WHERE g2.gene_symbol in ['FOXO','MTOR']
and g1.gene_symbol in ['PRKAB1', 'PRKAA1','PRKAA2']RETURN p
MATCH p = (g1:Gene) -[r*1..2 {datasource: 'metacore'}]-> (g2:Gene)WHERE g2.gene_symbol in ['FOXO','MTOR']
and g1.gene_symbol in ['PRKAB1', 'PRKAA1','PRKAA2']RETURN p, reduce(u=0.0, r in relationships(p) | u+r.uncertainty) as uncORDER BY unc LIMIT 20
Novartis Institutes for BioMedical Research (NIBR)Public27
Compound1
Compound2
Compound3
Compound4
Compound5
Compound6
Compound7
Compound8
Compound9
Compound10
Compound11
Compound12
Compound13
Novartis Institutes for BioMedical Research (NIBR)
Where (is this going)
• More tweaks to what we have– Improvements to text mining– Analysis of verbs (actions) / information extraction– Monitor change over time (what is new “emerging knowledge”)
• Full text analysis– Enable analysis and inclusion of internal documents
• Incorporate additional data sources– Gene Expression data (tissue expression and perturbations)– Mutations– Proteomics
• Refining the “uncertainty” measure– How best to compare uncertainties from different data sources
• Expand user base• Automated updates
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Novartis Institutes for BioMedical Research (NIBR)
• ISLD group– John Davies– Miguel Camargo– Eugen Lounkine– Elisabet Gregori-Puigjane– Mark Bray– Pierre Farmer– Ansgar Schuffenhauer
• Text mining group– Therese Vachon– Pierre Parrisot– Andrea Splendiani– Fatima Oezdemir-Zaech– Frederic Sutter
• Protein information:– Pfam: R.D. Finn, et. al. The Pfam protein families database: towards a more sustainable future, Nucleic Acids
Research (2016) Database Issue 44:D279-D285http://pfam.xfam.org/
– Uniprot: The UniProt Consortium, UniProt: a hub for protein information, Nucleic Acids Res. 43: D204-D212 (2015)http://www.uniprot.org/
• Comparative Toxicogenomics database:– Davis AP et. al. The Comparative Toxicogenomics Database's 10th year anniversary: update 2015. Nucleic Acids Res.
2015 Jan;43 (Database issue): D914-20.Curated chemical–gene data were retrieved from the Comparative Toxicogenomics Database (CTD), MDI Biological Laboratory, Salisbury Cove, Maine, and NC State University, Raleigh, North Carolina. World Wide Web (URL: http://ctdbase.org/). [May 2016].
• MetaCore
– Thomson Reuters LifeScienceshttp://thomsonreuters.com/en/products-services/pharma-life-sciences/pharmaceutical-research/metacore.html
• Protein-Protein interaction data set:– Center for Cancer Systems Biology (CCSB) at the Dana Farber Cancer Institute
http://ccsb.dfci.harvard.edu/
• Gene Ontology– The Gene Ontology Consortium. Gene Ontology Consortium: going forward. (2015) Nucl Acids Res 43 Database issue
D1049–D1056.http://geneontology.org/
• Pathways
– Reactome pathway database: A. Fabregat et. al., The Reactome pathway Knowledgebase, Nucl. Acids Res. (04 January 2016) 44 (D1): D481-D487D. Croft et. al., The Reactome pathway knowledgebase, Nucl. Acids Res. (1 January 2014) 42 (D1): D472-D477http://reactome.org/
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Acknowledgments / ReferencesSource References
• CPC– Sylvain Cottens– Doug Auld
• DMP– Jeremy Jenkins– Ben Cornett– Florian Nigsch
• NX– Stephen Litster
Thank you